title: 'Vector Engines' sidebar_label: 'Vector Engines' description: 'Configure vector engines for efficient embedding storage and similarity search in Spice.' sidebar_position: 8 pagination_prev: null pagination_next: null

🎓 Learn how it works with the Amazon S3 Vectors with Spice (opens in a new tab) engineering blog post.

Data sourced by Data Connectors, or views built atop them with vector embedding columns can be indexed and efficiently searched using a vector engine.

A vector engine will store all vector embeddings associated with columns in a dataset/view, provide efficient vector search operations and avoid unnecessary recomputation of embeddings.

A vector engine is configured by setting the vectors configuration. E.g.

datasets:
  - name: dataset_with_embeddings
    vectors:
      enabled: true

datasets:
  - name: dataset_with_embeddings
    vectors:
      enabled: true

For the complete reference specification see datasets (opens in a new tab).

Supported Vector engines:

:::warning[Limitations]

• A dataset or view must be accelerated (i.e. datasets[].acceleration.enabled: true, see docs (opens in a new tab)) for a vector engine to be provided the appropriate data to ingest.

  :::

Vector Engine Docs

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